code: def json2csv_preprocess(
      json_file,
      outfile,
      fields,
      encoding="utf8",
      errors="replace",
      gzip_compress=False,
      skip_retweets=True,
      skip_tongue_tweets=True,
      skip_ambiguous_tweets=True,
      strip_off_emoticons=True,
      remove_duplicates=True,
      limit=None,
  ):
      """
      Convert json file to csv file, preprocessing each row to obtain a suitable
      dataset for tweets Semantic Analysis.
  
      :param json_file: the original json file containing tweets.
      :param outfile: the output csv filename.
      :param fields: a list of fields that will be extracted from the json file and
          kept in the output csv file.
      :param encoding: the encoding of the files.
      :param errors: the error handling strategy for the output writer.
      :param gzip_compress: if True, create a compressed GZIP file.
  
      :param skip_retweets: if True, remove retweets.
      :param skip_tongue_tweets: if True, remove tweets containing ":P" and ":-P"
          emoticons.
      :param skip_ambiguous_tweets: if True, remove tweets containing both happy
          and sad emoticons.
      :param strip_off_emoticons: if True, strip off emoticons from all tweets.
      :param remove_duplicates: if True, remove tweets appearing more than once.
      :param limit: an integer to set the number of tweets to convert. After the
          limit is reached the conversion will stop. It can be useful to create
          subsets of the original tweets json data.
      """
      with codecs.open(json_file, encoding=encoding) as fp:
          (writer, outf) = _outf_writer(
              outfile, encoding, errors, gzip_compress
          )
          # write the list of fields as header
          writer.writerow(fields)
  
          if remove_duplicates == True:
              tweets_cache = []
          i = 0
          for line in fp:
              tweet = json.loads(line)
              row = extract_fields(tweet, fields)
              try:
                  text = row[fields.index("text")]
                  # Remove retweets
                  if skip_retweets == True:
                      if re.search(r"\bRT\b", text):
                          continue
                  # Remove tweets containing ":P" and ":-P" emoticons
                  if skip_tongue_tweets == True:
                      if re.search(r"\:\-?P\b", text):
                          continue
                  # Remove tweets containing both happy and sad emoticons
                  if skip_ambiguous_tweets == True:
                      all_emoticons = EMOTICON_RE.findall(text)
                      if all_emoticons:
                          if (set(all_emoticons) & HAPPY) and (
                              set(all_emoticons) & SAD
                          ):
                              continue
                  # Strip off emoticons from all tweets
                  if strip_off_emoticons == True:
                      row[fields.index("text")] = re.sub(
                          r"(?!\n)\s+", " ", EMOTICON_RE.sub("", text)
                      )
                  # Remove duplicate tweets
                  if remove_duplicates == True:
                      if row[fields.index("text")] in tweets_cache:
                          continue
                      else:
                          tweets_cache.append(row[fields.index("text")])
              except ValueError:
                  pass
              writer.writerow(row)
              i += 1
              if limit and i >= limit:
                  break
          outf.close()
  
imports: import codecs
  import csv
  import json
  import pickle
  import random
  import re
  import sys
  import time
  from copy import deepcopy
  import nltk
  from nltk.corpus import CategorizedPlaintextCorpusReader
  from nltk.data import load
  from nltk.tokenize.casual import EMOTICON_RE
label_desc: Use image node_id 8 for calling a global function with example usage: json2csv_preprocess(json_file, outfile, fields, encoding, errors, gzip_compress, skip_retweets, skip_tongue_tweets, skip_ambiguous_tweets, strip_off_emoticons, remove_duplicates, limit) without return types